Many image enhancement processes use image comparisons, wherein two or more images of the same object are compared. Examples of comparison techniques are subtraction, merger, and addition. The image subtraction process is especially used where changing or changed characteristics are of interest, such as in certain diagnostic medical imaging i.e. digital fluorography. There are two major subtraction methods in vogue i.e. temporal and energy. Of the two temporal subtraction is by far the more popular in medical applications while energy comparison is used more in land and crop surveillance. In both subtraction methods, the images compared must be aligned.
Temporal subtraction is generally accomplished either by "mask" or "TID" methods. In the "mask" method a selected prior image is subtracted from subsequent images. In the "TID" method, selected ones of a series of prior images are subtracted from selected ones of a series of subsequent images. In both cases it is apparent that the images used for subtraction must be in registration to provide artifact free results.
In digital fluorography e.g., the misregistration problem is especially aggravated by the subject's movement between the "mask" imaging and the subsequent images for example. The imaging sequence in DF normally takes approximately 15 seconds. While the subjects are instructed to hold their breath and not to swallow during this time period; nonetheless, they often do with resultant motion and misregistration.
The presently available re-registration methods; that is, methods which correct for misregistration, most commonly use either edge recognition and comparison or point recognition and comparison. Edges are relatively easy to recognize using differences or derivatives of some sort. However motions parallel to the edges are difficult to discern.
Points are relatively difficult to recognize. The maximum or minimum points are located in regions of small average gradients. Local "noise" also tends to play havoc with the linearity of the system and therefore move the measured point from its actual location to an apparent location. Low pass filters can be used to reduce the noise level but such filters also reduce the derivatives and therefore add an uncertainty to the actual location of the point.
"Global" points, for example, the center of gravity ("COG") of the data in some region of interest ("ROI") depends on the definition of the ROI and therefore have not been sufficiently definitive of the points for use in re-registration. For example, if the data is a step function with the step at a point X0 in the "mask" and at a point X1 in the image; an ROI that includes both points yields a shift in COG of (X1-X0)/2 when the shift of the point is really (X1-X0). To correctly define the shift a larger ROI that includes the opposite step is required. Such a large ROI will encompass complete organs and therefore data that has really changed, e.g. through inflow of contrast material. The correction for misregistration will therefore be incorrect.
Thus, there is presently in the known prior art no accurate and practical method or apparatus for recognizing the amount of the image shift and therefore no reliable protocol method of correcting for the misregistration caused by the image shifts.
Accordingly, it is an object of the present invention to provide means and methods for vectorally determining the image shift between images that are compared and/or subtracted one from the other.